comento logica de imagen

app.py

@@ -58,7 +58,7 @@ try:
                 # Initialize functions dictionary
                 functions = {
                     'generate_content': None,
-                    'generate_image': None,
+                    # 'generate_image': None,
                     'generate_content_with_llm': None
                 }                  
                 # Ensure app_name is defined
@@ -282,7 +282,7 @@ def generate_all_content(business_type, target_audience, content_goal, brand_nam
                     brand_name=brand_name, 
                     key_message=key_message,
                     model_name=selected_model,
-                    generate_images=generate_image_flag
+                    # generate_images=generate_image_flag
                 )
                 
                 # Check if we got a valid response
@@ -291,12 +291,12 @@ def generate_all_content(business_type, target_audience, content_goal, brand_nam
                     status_msg = f"✅ {result.get('message', 'Content generated')} in {time.time() - start_time:.2f}s"
                     
                     # Check for generated images
-                    if generate_image_flag and 'images' in result and result['images']:
-                        # Get the first platform with an image
-                        for platform in ['linkedin', 'instagram', 'tiktok', 'twitter', 'facebook']:
-                            if platform in result['images'] and result['images'][platform]:
-                                image_output = result['images'][platform]
-                                break
+                    # if generate_image_flag and 'images' in result and result['images']:
+                    #     # Get the first platform with an image
+                    #     for platform in ['linkedin', 'instagram', 'tiktok', 'twitter', 'facebook']:
+                    #         if platform in result['images'] and result['images'][platform]:
+                    #             image_output = result['images'][platform]
+                    #             break
                 else:
                     raise ValueError("Unexpected response format from Modal AI")
                     
@@ -455,12 +455,12 @@ with gr.Blocks(theme=gr.themes.Soft(), title="🚀 Content Creator Pro") as demo
                 )
             
             # Image generation toggle (only visible when AI generation is selected)
-            with gr.Group(visible=MODAL_AVAILABLE) as image_group:
-                generate_image_toggle = gr.Checkbox(
-                    label="🖼️ Generate AI Image",
-                    value=False,
-                    info="Check to generate an AI image based on your content"
-                )
+            # with gr.Group(visible=MODAL_AVAILABLE) as image_group:
+            #     generate_image_toggle = gr.Checkbox(
+            #         label="🖼️ Generate AI Image",
+            #         value=False,
+            #         info="Check to generate an AI image based on your content"
+            #     )
             
     run_button = gr.Button("🚀 Generate Content", variant="primary", size="lg")
     
@@ -475,15 +475,15 @@ with gr.Blocks(theme=gr.themes.Soft(), title="🚀 Content Creator Pro") as demo
             twitter_output = gr.Textbox(label="🐦 Twitter/𝕏 Post", lines=4)
             facebook_output = gr.Textbox(label="📘 Facebook Post", lines=6)
     
-    status = gr.Markdown("Ready to generate content!")
+    # status = gr.Markdown("Ready to generate content!")
     
     # Image output component
-    with gr.Row():
-        generated_image = gr.Image(
-            label="🖼️ Generated Image",
-            visible=False,
-            height=512
-        )
+    # with gr.Row():
+    #     generated_image = gr.Image(
+    #         label="🖼️ Generated Image",
+    #         visible=False,
+    #         height=512
+    #     )
     
     # Update component visibility based on modal toggle
     def update_modal_components_visibility(use_modal):
@@ -511,7 +511,7 @@ with gr.Blocks(theme=gr.themes.Soft(), title="🚀 Content Creator Pro") as demo
             key_message, 
             modal_toggle,
             model_selector,  # Add model selector to inputs
-            generate_image_toggle
+            # generate_image_toggle
         ],
         outputs=[
             linkedin_output, 
@@ -519,20 +519,20 @@ with gr.Blocks(theme=gr.themes.Soft(), title="🚀 Content Creator Pro") as demo
             tiktok_output, 
             twitter_output, 
             facebook_output, 
-            status,
-            generated_image
+            # status,
+            # generated_image
         ]
     )
     
     # Show/hide image based on whether an image was generated
-    def update_image_visibility(image_data):
-        return gr.Image(visible=image_data is not None), image_data or None
-    
-    generated_image.change(
-        fn=lambda x: (gr.Image(visible=x is not None), x or None),
-        inputs=[generated_image],
-        outputs=[generated_image, generated_image]
-    )
+    # def update_image_visibility(image_data):
+    #     return gr.Image(visible=image_data is not None), image_data or None
+    
+    # generated_image.change(
+    #     fn=lambda x: (gr.Image(visible=x is not None), x or None),
+    #     inputs=[generated_image],
+    #     outputs=[generated_image, generated_image]
+    # )
     
     # Add examples
     gr.Examples(

modal_backend.py

@@ -726,104 +726,104 @@ Important:
 
 
 # Image generation function
-@app.function(
-    image=image,
-    gpu="A10G",
-    memory=32768,
-    secrets=[modal.Secret.from_name("huggingface-secret")],
-    timeout=1200,  # 20 minutes
-    volumes={CACHE_DIR: volume},
-)
-def generate_image(
-    prompt: str, 
-    negative_prompt: str = "", 
-    num_images: int = 1,
-    width: int = 1024,
-    height: int = 1024
-) -> list:
-    """
-    Generate high-quality images using Stable Diffusion XL 1.0
+# @app.function(
+#     image=image,
+#     gpu="A10G",
+#     memory=32768,
+#     secrets=[modal.Secret.from_name("huggingface-secret")],
+#     timeout=1200,  # 20 minutes
+#     volumes={CACHE_DIR: volume},
+# )
+# def generate_image(
+#     prompt: str, 
+#     negative_prompt: str = "", 
+#     num_images: int = 1,
+#     width: int = 1024,
+#     height: int = 1024
+# ) -> list:
+#     """
+#     Generate high-quality images using Stable Diffusion XL 1.0
     
-    Args:
-        prompt: Text description of the image to generate
-        negative_prompt: Text description of what not to include in the image
-        num_images: Number of images to generate (1-4)
-        width: Width of the generated image (multiple of 8, max 1024)
-        height: Height of the generated image (multiple of 8, max 1024)
-        
-    Returns:
-        List of base64-encoded image strings
-    """
-    from diffusers import StableDiffusionXLPipeline
-    import torch
-    from io import BytesIO
-    import base64
+#     Args:
+#         prompt: Text description of the image to generate
+#         negative_prompt: Text description of what not to include in the image
+#         num_images: Number of images to generate (1-4)
+#         width: Width of the generated image (multiple of 8, max 1024)
+#         height: Height of the generated image (multiple of 8, max 1024)
+        
+#     Returns:
+#         List of base64-encoded image strings
+#     """
+#     from diffusers import StableDiffusionXLPipeline
+#     import torch
+#     from io import BytesIO
+#     import base64
     
-    try:
-        print(f"🖼️ Generating {num_images} image(s) with prompt: {prompt[:100]}...")
-        
-        # Load the model with caching
-        model_id = "stabilityai/stable-diffusion-xl-base-1.0"
-        
-        print("Loading Stable Diffusion XL pipeline...")
-        pipe = StableDiffusionXLPipeline.from_pretrained(
-            model_id,
-            torch_dtype=torch.float16,
-            use_safetensors=True,
-            variant="fp16",
-            cache_dir=CACHE_DIR,
-            local_files_only=False  # Allow downloading if not in cache
-        )
-        
-        # Move to GPU if available
-        if torch.cuda.is_available():
-            pipe = pipe.to("cuda")
-            print("✅ Moved model to GPU")
-        
-        # Generate images
-        print(f"⚡ Generating {num_images} image(s) with size {width}x{height}...")
-        images = pipe(
-            prompt=prompt,
-            negative_prompt=negative_prompt,
-            num_images_per_prompt=num_images,
-            num_inference_steps=30,
-            guidance_scale=7.5,
-            width=width,
-            height=height,
-        ).images
-        
-        print(f"✅ Generated {len(images)} images successfully")
-        
-        # Convert PIL images to base64
-        result = []
-        for i, img in enumerate(images):
-            buffered = BytesIO()
-            img.save(buffered, format="PNG")
-            img_str = base64.b64encode(buffered.getvalue()).decode()
-            result.append(f"data:image/png;base64,{img_str}")
-            print(f"  - Image {i+1}: {len(img_str) // 1000} KB")
-        
-        return result
-        
-    except Exception as e:
-        error_msg = f"Error generating content: {str(e)}"
-        print(f"\n❌ {error_msg}")
-        import traceback
-        traceback.print_exc()
-        
-        # Return a more helpful error message
-        error_details = {
-            "error": "Failed to generate content",
-            "message": str(e),
-            "type": type(e).__name__,
-            "platforms": PLATFORMS
-        }
-        
-        # Log the full error for debugging
-        print(f"\n⚠️ Error details: {error_details}")
-        
-        # Return error for each platform
-        return [f"Error: {error_msg}"]
+#     try:
+#         print(f"🖼️ Generating {num_images} image(s) with prompt: {prompt[:100]}...")
+        
+#         # Load the model with caching
+#         model_id = "stabilityai/stable-diffusion-xl-base-1.0"
+        
+#         print("Loading Stable Diffusion XL pipeline...")
+#         pipe = StableDiffusionXLPipeline.from_pretrained(
+#             model_id,
+#             torch_dtype=torch.float16,
+#             use_safetensors=True,
+#             variant="fp16",
+#             cache_dir=CACHE_DIR,
+#             local_files_only=False  # Allow downloading if not in cache
+#         )
+        
+#         # Move to GPU if available
+#         if torch.cuda.is_available():
+#             pipe = pipe.to("cuda")
+#             print("✅ Moved model to GPU")
+        
+#         # Generate images
+#         print(f"⚡ Generating {num_images} image(s) with size {width}x{height}...")
+#         images = pipe(
+#             prompt=prompt,
+#             negative_prompt=negative_prompt,
+#             num_images_per_prompt=num_images,
+#             num_inference_steps=30,
+#             guidance_scale=7.5,
+#             width=width,
+#             height=height,
+#         ).images
+        
+#         print(f"✅ Generated {len(images)} images successfully")
+        
+#         # Convert PIL images to base64
+#         result = []
+#         for i, img in enumerate(images):
+#             buffered = BytesIO()
+#             img.save(buffered, format="PNG")
+#             img_str = base64.b64encode(buffered.getvalue()).decode()
+#             result.append(f"data:image/png;base64,{img_str}")
+#             print(f"  - Image {i+1}: {len(img_str) // 1000} KB")
+        
+#         return result
+        
+#     except Exception as e:
+#         error_msg = f"Error generating content: {str(e)}"
+#         print(f"\n❌ {error_msg}")
+#         import traceback
+#         traceback.print_exc()
+        
+#         # Return a more helpful error message
+#         error_details = {
+#             "error": "Failed to generate content",
+#             "message": str(e),
+#             "type": type(e).__name__,
+#             "platforms": PLATFORMS
+#         }
+        
+#         # Log the full error for debugging
+#         print(f"\n⚠️ Error details: {error_details}")
+        
+#         # Return error for each platform
+#         return [f"Error: {error_msg}"]
 
 # Combined function to generate both text and images
 @app.function(
@@ -840,7 +840,7 @@ def generate_content(
     content_goal: str,
     brand_name: str,
     key_message: str,
-    generate_images: bool = False,
+    # generate_images: bool = False,
     model_name: str = DEFAULT_MODEL,
 ):
     """
@@ -878,39 +878,39 @@ def generate_content(
                     print(f"  ✅ {platform} content generated successfully!")
                     
                     # Generate image if requested
-                    if generate_images and platform_content and not platform_content.startswith("Error"):
-                        try:
-                            print(f"  🖼️  Generating {platform} image...")
-                            image_prompt = (
-                                f"Create an engaging social media image for {platform} about: {key_message}. "
-                                f"Business type: {business_type}. Target audience: {target_audience}. "
-                                f"Style: professional, high-quality, on-brand for {brand_name}. "
-                                "8k, detailed, cinematic lighting"
-                            )
+                    # if generate_images and platform_content and not platform_content.startswith("Error"):
+                    #     try:
+                    #         print(f"  🖼️  Generating {platform} image...")
+                    #         image_prompt = (
+                    #             f"Create an engaging social media image for {platform} about: {key_message}. "
+                    #             f"Business type: {business_type}. Target audience: {target_audience}. "
+                    #             f"Style: professional, high-quality, on-brand for {brand_name}. "
+                    #             "8k, detailed, cinematic lighting"
+                    #         )
                             
-                            image_url = generate_image.remote(
-                                prompt=image_prompt,
-                                negative_prompt=(
-                                    "low quality, blurry, text, watermark, logo, "
-                                    "distorted, deformed, disfigured, bad anatomy, "
-                                    "extra limbs, extra fingers, cropped, jpeg artifacts"
-                                ),
-                                num_images=1,
-                                width=1024,
-                                height=1024
-                            )
+                    #         image_url = generate_image.remote(
+                    #             prompt=image_prompt,
+                    #             negative_prompt=(
+                    #                 "low quality, blurry, text, watermark, logo, "
+                    #                 "distorted, deformed, disfigured, bad anatomy, "
+                    #                 "extra limbs, extra fingers, cropped, jpeg artifacts"
+                    #             ),
+                    #             num_images=1,
+                    #             width=1024,
+                    #             height=1024
+                    #         )
                             
-                            if image_url and not any(url.startswith("Error") for url in image_url):
-                                image_results[platform] = image_url[0]
-                                print(f"  ✅ {platform} image generated successfully!")
-                            else:
-                                image_results[platform] = f"Failed to generate {platform} image"
-                                print(f"  ❌ Failed to generate {platform} image")
+                    #         if image_url and not any(url.startswith("Error") for url in image_url):
+                    #             image_results[platform] = image_url[0]
+                    #             print(f"  ✅ {platform} image generated successfully!")
+                    #         else:
+                    #             image_results[platform] = f"Failed to generate {platform} image"
+                    #             print(f"  ❌ Failed to generate {platform} image")
                                 
-                        except Exception as img_e:
-                            error_msg = f"Error generating {platform} image: {str(img_e)}"
-                            print(f"  ❌ {error_msg}")
-                            image_results[platform] = error_msg
+                    #     except Exception as img_e:
+                    #         error_msg = f"Error generating {platform} image: {str(img_e)}"
+                    #         print(f"  ❌ {error_msg}")
+                    #         image_results[platform] = error_msg
                             
                 except Exception as e:
                     error_msg = f"Error generating {platform} content: {str(e)[:200]}"
@@ -920,7 +920,7 @@ def generate_content(
             return {
                 "status": "success",
                 "content": content_results,
-                "images": image_results if generate_images else {},
+                # "images": image_results if generate_images else {},
                 "message": "Content generated successfully"
             }
             
@@ -974,7 +974,7 @@ if __name__ == "__main__":
             content_goal="Generate leads with our new social media strategy",
             brand_name="GrowthHack Marketing",
             key_message="Our proven 3-step framework can help you 3x your online visibility in just 30 days.",
-            generate_images=True,
+            # generate_images=True,
             image_width=1024,
             image_height=1024
         )